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Mid-level, metro-based MLOps role at a known analytics firm creates high candidate competition.
MLOps skills are transferable across industries but require ML-specific experience, so background fit sensitivity is medium.
Explicit 3-5 years plus mandatory MLOps tools, CI/CD, and cloud experience makes shortlisting highly strict.
Collaborate with Data Scientists and Data Engineers to deploy and operate advanced ML models and automate model development and operations.
Build and maintain scalable ML pipelines and MLOps components using tools like MLFlow, Kubeflow, and other ML platform services on cloud or on-prem environments.
Troubleshoot and resolve issues in development, testing, and production; contribute to client business development and delivery across multiple domains.
3-5 years experience building production-quality software especially in System Integration, Application Development, or DataWarehouse projects.
Proficient in object-oriented programming languages such as Python, PySpark, Java, C#, or C++ and basic knowledge of MLOps, ML, Docker.
Experience with CI/CD pipelines for ML, SQL databases, Git source control, and foundational knowledge of at least one major cloud platform (AWS, Azure, or GCP).
Bachelor’s or Master’s degree in Computer Science or related technical field (B.E/B.Tech/M.Tech or equivalent).
Hands-on experience across all phases of the ML development life cycle with a focus on MLOps automation and scalable pipeline development.
Comfortable working in fast-paced, collaborative environments involving cross-functional teams and client engagements.
Experienced in building cloud-based and on-prem ML deployment and monitoring solutions with problem-solving and project management capabilities.